The present invention generally relates to modeling systems, and more particularly, to a method for microgrid controls development.
Microgrids are local implementations of a power distribution system that emulate the operation of standard utility grids on a smaller scale. These systems are a rapidly growing segment of the power industry. In general, they are required to manage sources, loads and storage systems to optimize availability, economy, reliability, etc. Sources used in microgrid systems can vary widely. For example, utility grids, diesel generators, wind turbines, photovoltaic systems, and gas turbines are some sources tied to a microgrid system. Microgrids can deliver power as AC or DC. The loads a microgrid system supplies can also be AC or DC in nature, and vary widely in their power requirements both initially and over time. In addition, it is often desirable to assign different priorities to loads to ensure that critical loads are given top priority in case the power available is insufficient to supply all loads. Also, microgrid systems may include storage systems configured as energy reservoirs that typically store energy when it is readily available or inexpensive, and may act as secondary sources for the system loads when it is advantageous to employ them. The typical microgrid system should accommodate these diverse subsystems and optimize their management.
The power in a microgrid system may be routed from sources to loads by means of circuits controlled by ‘switchgear’, which is a class of devices designed for their power handling characteristics. Switchgear typically have a binary control characteristic (two allowed states; on/off). Sources are generally managed as two-state components (on/off), with the classic analog control functions (such as engine throttle in the case of diesel generators) implemented as embedded subsystem functions.
The proliferation of switchgear and the associated control signals in modern microgrids leads to what is termed in mathematics and computer science as a ‘combinatorial explosion’. If there are n Boolean state variables associated with a microgrid there will be 2^n possible states. For example, a typical microgrid system may include 15 switchgear variables associated with sources and 13 with loads. If the system comprises 4 additional switching components there is a complexity on the order of 2^(15+13+4)=2^32=1024 possible states. In addition, if each of the 13 loads can be assigned one of three priority levels this leads to a subordinate set of 3^13=2,197 possible load configurations that must be effectively managed within the larger context.
Conventional microgrid modeling and simulation to date is concerned with classic controls solutions (typically PID controllers, or a subset, that employ feedback to minimize an error signal and thus ensure stable operation), or modeling that tries to capture the dynamics of complex power (real and reactive), as with generators and switched loads, again with an eye to improve stability and to allow design engineers to properly specify system components. The latter are typically computationally demanding. A system comprising two simplified 3-phase generator models in parallel driving common loads may require 20 minutes of simulation time to compute a 5 second response interval on a typical workstation. Simulations involving many more components and 24 hour intervals are impractical.
As can be seen, there is a need for a means to methodically develop microgrid control algorithms which address system complexity and efficiently verify their performance.